OpenRaiser/Intern-Atlas huggingface dataset:
Transfer Learning with Dynamic Adversarial Adaptation Network
Venue: arXiv cs.LG 2019
Year: 2019

The recent advances in deep transfer learning reveal that adversarial learning can be embedded into deep networks to learn more transferable features to reduce the distribution discrepancy between two domains. Existing adversarial domain adaptation methods either learn a single domain discriminator to align the global source and target distributions or pay attention to align subdomains based on multiple discriminators. However, in real applications, the marginal (global) and conditional (local) distributions between domains are often contributing differently to the adaptation. There is currently no method to dynamically and quantitatively evaluate the relative importance of these two distributions for adversarial learning. In this paper, we propose a novel Dynamic Adversarial Adaptation Network (DAAN) to dynamically learn domain-invariant representations while quantitatively evaluate the relative importance of global and local domain distributions. To the best of our knowledge, DAAN is the first attempt to perform dynamic adversarial distribution adaptation for deep adversarial learning. DAAN is extremely easy to implement and train in real applications. We theoretically analyze the effectiveness of DAAN, and it can also be explained in an attention strategy. Extensive experiments demonstrate that DAAN achieves better classification accuracy compared to state-of-the-art deep and adversarial methods. Results also imply the necessity and effectiveness of the dynamic distribution adaptation in adversarial transfer learning.
Learned-SBL: A Deep Learning Architecture for Sparse Signal Recovery
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we present a computationally efficient sparse signal recovery scheme using Deep Neural Networks (DNN). The architecture of the introduced neural network is inspired from sparse Bayesian learning (SBL) and named as Learned-SBL (L-SBL). We design a common architecture to recover sparse as well as block sparse vectors from single measurement vector (SMV) or multiple measurement vectors (MMV) depending on the nature of the training data. In the MMV model, the L-SBL network can be trained to learn any underlying sparsity pattern among the vectors including joint sparsity, block sparsity, etc. In particular, for block sparse recovery, learned-SBL does not require any prior knowledge of block boundaries. In each layer of the L-SBL, an estimate of the signal covariance matrix is obtained as the output of a neural network. Then a maximum a posteriori (MAP) estimator of the unknown sparse vector is implemented with non-trainable parameters. In many applications, the measurement matrix may be time-varying. The existing DNN based sparse signal recovery schemes demand the retraining of the neural network using current measurement matrix. The architecture of L-SBL allows it to accept the measurement matrix as an input to the network, and thereby avoids the need for retraining. We also evaluate the performance of Learned-SBL in the detection of an extended target using a multiple-input multiple-output (MIMO) radar. Simulation results illustrate that the proposed approach offers superior sparse recovery performance compared to the state-of-the-art methods.
Sample-specific repetitive learning for photo aesthetic assessment and highlight region extraction
Venue: arXiv cs.CV 2019
Year: 2019

Aesthetic assessment is subjective, and the distribution of the aesthetic levels is imbalanced. In order to realize the auto-assessment of photo aesthetics, we focus on retraining the CNN-based aesthetic assessment model by dropping out the unavailable samples in the middle levels from the training data set repetitively to overcome the effect of imbalanced aesthetic data on classification. Further, the method of extracting aesthetics highlight region of the photo image by using the two repetitively trained models is presented. Therefore, the correlation of the extracted region with the aesthetic levels is analyzed to illustrate what aesthetics features influence the aesthetic quality of the photo. Moreover, the testing data set is from the different data source called 500px. Experimental results show that the proposed method is effective.
Prolog Coding Guidelines: Status and Tool Support
Venue: arXiv cs.PL 2019
Year: 2019

The importance of coding guidelines is generally accepted throughout developers of every programming language. Naturally, Prolog makes no exception. However, establishing coding guidelines is fraught with obstacles: Finding common ground on kind and selection of rules is matter of debate; once found, adhering to or enforcing rules is complicated as well, not least because of Prolog's flexible syntax without keywords. In this paper, we evaluate the status of coding guidelines in the Prolog community and discuss to what extent they can be automatically verified. We implemented a linter for Prolog and applied it to several packages to get a hold on the current state of the community.
A Three-Valued Semantics for Typed Logic Programming
Venue: arXiv cs.LO 2019
Year: 2019

Types in logic programming have focused on conservative approximations of program semantics by regular types, on one hand, and on type systems based on a prescriptive semantics defined for typed programs, on the other. In this paper, we define a new semantics for logic programming, where programs evaluate to true, false, and to a new semantic value called wrong, corresponding to a run-time type error. We then have a type language with a separated semantics of types. Finally, we define a type system for logic programming and prove that it is semantically sound with respect to a semantic relation between programs and types where, if a program has a type, then its semantics is not wrong. Our work follows Milner's approach for typed functional languages where the semantics of programs is independent from the semantic of types, and the type system is proved to be sound with respect to a relation between both semantics.
Epistemic Logic Programs: A Different World View
Venue: arXiv cs.LO 2019
Year: 2019

Epistemic Logic Programs (ELPs), an extension of Answer Set Programming (ASP) with epistemic operators, have received renewed attention from the research community in recent years. Classically, evaluating an ELP yields a set of world views, with each being a set of answer sets. In this paper, we propose an alternative definition of world views that represents them as three-valued assignments, where each atom can be either always true, always false, or neither. Based on several examples, we show that this definition is natural and intuitive. We also investigate relevant computational properties of these new semantics, and explore how other notions, like strong equivalence, are affected.
A Rule-Based System for Explainable Donor-Patient Matching in Liver Transplantation
Venue: arXiv cs.LO 2019
Year: 2019

In this paper we present web-liver, a rule-based system for decision support in the medical domain, focusing on its application in a liver transplantation unit for implementing policies for donor-patient matching. The rule-based system is built on top of an interpreter for logic programs with partial functions, called lppf, that extends the paradigm of Answer Set Programming (ASP) adding two main features: (1) the inclusion of partial functions and (2) the computation of causal explanations for the obtained solutions. The final goal of web-liver is assisting the medical experts in the design of new donor-patient matching policies that take into account not only the patient severity but also the transplantation utility. As an example, we illustrate the tool behaviour with a set of rules that implement the utility index called SOFT.
BigData Applications from Graph Analytics to Machine Learning by Aggregates in Recursion
Venue: arXiv cs.LO 2019
Year: 2019

In the past, the semantic issues raised by the non-monotonic nature of aggregates often prevented their use in the recursive statements of logic programs and deductive databases. However, the recently introduced notion of Pre-mappability (PreM) has shown that, in key applications of interest, aggregates can be used in recursion to optimize the perfect-model semantics of aggregate-stratified programs. Therefore we can preserve the declarative formal semantics of such programs while achieving a highly efficient operational semantics that is conducive to scalable implementations on parallel and distributed platforms. In this paper, we show that with PreM, a wide spectrum of classical algorithms of practical interest, ranging from graph analytics and dynamic programming based optimization problems to data mining and machine learning applications can be concisely expressed in declarative languages by using aggregates in recursion. Our examples are also used to show that PreM can be checked using simple techniques and templatized verification strategies. A wide range of advanced BigData applications can now be expressed declaratively in logic-based languages, including Datalog, Prolog, and even SQL, while enabling their execution with superior performance and scalability.
A Temporal Module for Logical Frameworks
Venue: arXiv cs.AI 2019
Year: 2019

In artificial intelligence, multi agent systems constitute an interesting typology of society modeling, and have in this regard vast fields of application, which extend to the human sciences. Logic is often used to model such kind of systems as it is easier to verify than other approaches, and provides explainability and potential validation. In this paper we define a time module suitable to add time to many logic representations of agents.
SINR Analysis of Different Multicarrier Waveforms over Doubly Dispersive Channels
Venue: arXiv cs.IT 2019
Year: 2019

Wireless channels generally exhibit dispersion in both time and frequency domain, known as doubly selective or doubly dispersive channels. To combat the delay spread effect, multicarrier modulation (MCM) such as orthogonal frequency division multiplexing (OFDM) and its universal filtered variant (UF-OFDM) is employed, leading to the simple per-subcarrier one tap equalization. The time-varying nature of the channel, in particular, the intra-multicarrier-symbol channel variation induces spectral broadening and thus inter-carrier interference (ICI). Existing works address both effects separately, focus on the one effect while ignoring the respective other. This paper considers both effect simultaneously for cyclic prefix (CP)-, zero padded (ZP)- and UF-based OFDM with simple one tap equalization, assuming a general wireless channel model. For this general channel model, we show that the independent (wide sense stationary uncorrelated scatter, WSSUS) selectivity in time and frequency starts to intertwine in contrast to the ideal cases with single selectivity. We derive signal-to-interference-plus-noise ratio (SINR) in closed form for arbitrary system settings and channel parameters, e.g., bandwidth, delay- and Doppler-spread. With the SINR analysis, we compare the three MCM schemes under different channel scenarios.
Limited-budget output consensus for descriptor multiagent systems with energy constraints
Venue: arXiv cs.MA 2019
Year: 2019

The current paper deals with limited-budget output consensus for descriptor multiagent systems with two types of switching communication topologies; that is, switching connected ones and jointly connected ones. Firstly, a singular dynamic output feedback control protocol with switching communication topologies is proposed on the basis of the observable decomposition, where an energy constraint is involved and protocol states of neighboring agents are utilized to derive a new two-step design approach of gain matrices. Then, limited-budget output consensus problems are transformed into asymptotic stability ones and a valid candidate of the output consensus function is determined. Furthermore, sufficient conditions for limited-budget output consensus design for two types of switching communication topologies are proposed, respectively. Finally, two numerical simulations are shown to demonstrate theoretical conclusions.
Stopping Criteria for Value and Strategy Iteration on Concurrent Stochastic Reachability Games
Venue: arXiv cs.GT 2019
Year: 2019

We consider concurrent stochastic games played on graphs with reachability and safety objectives. These games can be solved by value iteration as well as strategy iteration, each of them yielding a sequence of under-approximations of the reachability value and a sequence of over-approximation of the safety value, converging to it in the limit. For both approaches, we provide the first (anytime) algorithms with stopping criteria. The stopping criterion for value iteration is based on providing a convergent sequence of over-approximations, which then allows to estimate the distance to the true value. For strategy iteration, we bound the error by complementing the strategy iteration algorithm for reachability by a new strategy iteration algorithm under-approximating the safety-value.
Non-Interactive Private Decision Tree Evaluation
Venue: arXiv cs.CR 2019
Year: 2019

Decision trees are a powerful prediction model with many applications in statistics, data mining, and machine learning. In some settings, the model and the data to be classified may contain sensitive information belonging to different parties. In this paper, we, therefore, address the problem of privately evaluating a decision tree on private data. This scenario consists of a server holding a private decision tree model and a client interested in classifying its private attribute vector using the server's private model. The goal of the computation is to obtain the classification while preserving the privacy of both - the decision tree and the client input. After the computation, the classification result is revealed only to the client, and nothing else is revealed neither to the client nor to the server. Existing privacy-preserving protocols that address this problem use or combine different generic secure multiparty computation approaches resulting in several interactions between the client and the server. Our goal is to design and implement a novel client-server protocol that delegates the complete tree evaluation to the server while preserving privacy and reducing the overhead. The idea is to use fully (somewhat) homomorphic encryption and evaluate the tree on ciphertexts encrypted under the client's public key. However, since current somewhat homomorphic encryption schemes have high overhead, we combine efficient data representations with different algorithmic optimizations to keep the computational overhead and the communication cost low. As a result, we are able to provide the first non-interactive protocol, that allows the client to delegate the evaluation to the server by sending an encrypted input and receiving only the encryption of the result. Our scheme has only one round and can evaluate a complete tree of depth 10 within seconds.
5G QoS: Impact of Security Functions on Latency
Venue: arXiv cs.NI 2019
Year: 2019

Network slicing is considered a key enabler to 5th Generation (5G) communication networks. Mobile network operators may deploy network slices -- complete logical networks customized for specific services expecting a certain Quality of Service (QoS). New business models like Network Slice-as-a-Service offerings to customers from vertical industries require negotiated Service Level Agreement (SLA) contracts, and network providers need automated enforcement mechanisms to assure QoS during instantiation and operation of slices. In this paper, we focus on ultra-reliable low-latency communication (URLLC). We propose a software architecture for security functions based on off-the-shelf hardware and open-source software and demonstrate, through a series of measurements, that the strict requirements of URLLC services can be achieved. As a real-world example, we perform our experiments using the intrusion prevention system (IPS) Snort to demonstrate the impact of security functions on latency. Our findings lead to the creation of a model predicting the system load that still meets the URLLC latency requirement. We fully disclose the artifacts presented in this paper including pcap traces, measurement tools, and plotting scripts at https://gallenmu.github.io/low-latency.
Semantically Interpretable Activation Maps: what-where-how explanations within CNNs
Venue: arXiv cs.CV 2019
Year: 2019

A main issue preventing the use of Convolutional Neural Networks (CNN) in end user applications is the low level of transparency in the decision process. Previous work on CNN interpretability has mostly focused either on localizing the regions of the image that contribute to the result or on building an external model that generates plausible explanations. However, the former does not provide any semantic information and the latter does not guarantee the faithfulness of the explanation. We propose an intermediate representation composed of multiple Semantically Interpretable Activation Maps (SIAM) indicating the presence of predefined attributes at different locations of the image. These attribute maps are then linearly combined to produce the final output. This gives the user insight into what the model has seen, where, and a final output directly linked to this information in a comprehensive and interpretable way. We test the method on the task of landscape scenicness (aesthetic value) estimation, using an intermediate representation of 33 attributes from the SUN Attributes database. The results confirm that SIAM makes it possible to understand what attributes in the image are contributing to the final score and where they are located. Since it is based on learning from multiple tasks and datasets, SIAM improve the explanability of the prediction without additional annotation efforts or computational overhead at inference time, while keeping good performances on both the final and intermediate tasks.
Introduction to the Tezos Blockchain
Venue: arXiv cs.DC 2019
Year: 2019

Tezos is an innovative blockchain that improves on several aspects compared to more established blockchains. It offers an original proof-of-stake consensus algorithm and can be used as a decentralized smart contract platform. It has the capacity to amend its own economic protocol through a voting mechanism and focuses on formal methods to improve safety.
Cutting Music Source Separation Some Slakh: A Dataset to Study the Impact of Training Data Quality and Quantity
Venue: arXiv cs.SD 2019
Year: 2019

Music source separation performance has greatly improved in recent years with the advent of approaches based on deep learning. Such methods typically require large amounts of labelled training data, which in the case of music consist of mixtures and corresponding instrument stems. However, stems are unavailable for most commercial music, and only limited datasets have so far been released to the public. It can thus be difficult to draw conclusions when comparing various source separation methods, as the difference in performance may stem as much from better data augmentation techniques or training tricks to alleviate the limited availability of training data, as from intrinsically better model architectures and objective functions. In this paper, we present the synthesized Lakh dataset (Slakh) as a new tool for music source separation research. Slakh consists of high-quality renderings of instrumental mixtures and corresponding stems generated from the Lakh MIDI dataset (LMD) using professional-grade sample-based virtual instruments. A first version, Slakh2100, focuses on 2100 songs, resulting in 145 hours of mixtures. While not fully comparable because it is purely instrumental, this dataset contains an order of magnitude more data than MUSDB18, the {\it de facto} standard dataset in the field. We show that Slakh can be used to effectively augment existing datasets for musical instrument separation, while opening the door to a wide array of data-intensive music signal analysis tasks.
Parallel Concatenation of Non-Binary Linear Random Fountain Codes with Maximum Distance Separable Codes
Venue: arXiv cs.IT 2019
Year: 2019

The performance and the decoding complexity of a novel coding scheme based on the concatenation of maximum distance separable (MDS) codes and linear random fountain codes are investigated. Differently from Raptor codes (which are based on a serial concatenation of a high-rate outer block code and an inner Luby-transform code), the proposed coding scheme can be seen as a parallel concatenation of a MDS code and a linear random fountain code, both operating on the same finite field. Upper and lower bounds on the decoding failure probability under maximum-likelihood (ML) decoding are developed. It is shown how, for example, the concatenation of a $(15,10)$ Reed-Solomon (RS) code and a linear random fountain code over a finite field of order $16$, $\mathbb {F}_{16}$, brings to a decoding failure probability $4$ orders of magnitude lower than the one of a linear random fountain code for the same receiver overhead in a channel with a erasure probability of $ε=5\cdot10^{-2}$. It is illustrated how the performance of the novel scheme approaches that of an idealized fountain code for higher-order fields and moderate erasure probabilities. An efficient decoding algorithm is developed for the case of a (generalized) RS code.
An Automated Engineering Assistant: Learning Parsers for Technical Drawings
Venue: arXiv cs.AI 2019
Year: 2019

From a set of technical drawings and expert knowledge, we automatically learn a parser to interpret such a drawing. This enables automatic reasoning and learning on top of a large database of technical drawings. In this work, we develop a similarity based search algorithm to help engineers and designers find or complete designs more easily and flexibly. This is part of an ongoing effort to build an automated engineering assistant. The proposed methods make use of both neural methods to learn to interpret images, and symbolic methods to learn to interpret the structure in the technical drawing and incorporate expert knowledge.
Class Feature Pyramids for Video Explanation
Venue: arXiv cs.CV 2019
Year: 2019

Deep convolutional networks are widely used in video action recognition. 3D convolutions are one prominent approach to deal with the additional time dimension. While 3D convolutions typically lead to higher accuracies, the inner workings of the trained models are more difficult to interpret. We focus on creating human-understandable visual explanations that represent the hierarchical parts of spatio-temporal networks. We introduce Class Feature Pyramids, a method that traverses the entire network structure and incrementally discovers kernels at different network depths that are informative for a specific class. Our method does not depend on the network's architecture or the type of 3D convolutions, supporting grouped and depth-wise convolutions, convolutions in fibers, and convolutions in branches. We demonstrate the method on six state-of-the-art 3D convolution neural networks (CNNs) on three action recognition (Kinetics-400, UCF-101, and HMDB-51) and two egocentric action recognition datasets (EPIC-Kitchens and EGTEA Gaze+).